Face and Emotion Recognition with Neural Networks on Mobile Devices: Practical Implementation on Different Platforms

We propose a novel architecture for face and emotion recognition and discuss modifications for different types of mobile applications. Emotion recognition task is challenging due to the absence of large-scale datasets and non-uniform labelling. We propose easy-to-implement five class classification approach and suggest modifications for large-scale emotion recognition on three different platforms: desktop, mobile and VPU, and compare the resulting speed and performance. We demonstrate that our results can be compared with the performance of state-of-the-art neural networks and can be implemented on mobile and stand-alone devices.